Datasets - overview of data
Datasets - cleaning, augmenting and joining
Methods - study design
Covid-19 cases and deaths in each country
Results - variable selection - Mette Chr
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 200.098826 | 11.376201 | 17.589248 | 0.000000 |
| life_expectancy | -0.960407 | 0.192274 | -4.995000 | 0.000002 |
| population_living_in_urban_areas | -0.166332 | 0.062027 | -2.681600 | 0.008278 |
| respiratory_diseases | -156.620453 | 54.571333 | -2.870013 | 0.004793 |
Respiratory diseases
Life expectancy
Population % living in urban areas
PCA analysis by population demographics
- PCA showed a clear association with COVID-19 kinetics
- Relative COVID-19 deaths were more informative than absolute deaths
- PC1 comprises 44.6% of variation
PCA and cluster analysis
- Cluster analysis (n=3) based on population demographics data (middle) and on PCA (right)
- Cluster analysis does not capture COVID-19 kinetics accurately
Shiny app - Mette Chr
Another explanation?
Another explanation?
OR JUST CONFOUNDING BY DEVELOPMENTAL STATUS OF THE COUNTRIES… 